本文整理匯總了Python中object_detection.core.preprocessor.random_black_patches方法的典型用法代碼示例。如果您正苦於以下問題：Python preprocessor.random_black_patches方法的具體用法？Python preprocessor.random_black_patches怎麽用？Python preprocessor.random_black_patches使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類object_detection.core.preprocessor的用法示例。
在下文中一共展示了preprocessor.random_black_patches方法的3個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: testRandomBlackPatches
# 需要導入模塊: from object_detection.core import preprocessor [as 別名]
# 或者: from object_detection.core.preprocessor import random_black_patches [as 別名]
def testRandomBlackPatches(self):
preprocessing_options = []
preprocessing_options.append((preprocessor.normalize_image, {
'original_minval': 0,
'original_maxval': 255,
'target_minval': 0,
'target_maxval': 1
}))
preprocessing_options.append((preprocessor.random_black_patches, {
'size_to_image_ratio': 0.5
}))
images = self.createTestImages()
tensor_dict = {fields.InputDataFields.image: images}
blacked_tensor_dict = preprocessor.preprocess(tensor_dict,
preprocessing_options)
blacked_images = blacked_tensor_dict[fields.InputDataFields.image]
images_shape = tf.shape(images)
blacked_images_shape = tf.shape(blacked_images)
with self.test_session() as sess:
(images_shape_, blacked_images_shape_) = sess.run(
[images_shape, blacked_images_shape])
self.assertAllEqual(images_shape_, blacked_images_shape_) 示例2: testRandomBlackPatches
# 需要導入模塊: from object_detection.core import preprocessor [as 別名]
# 或者: from object_detection.core.preprocessor import random_black_patches [as 別名]
def testRandomBlackPatches(self):
def graph_fn():
preprocessing_options = []
preprocessing_options.append((preprocessor.normalize_image, {
'original_minval': 0,
'original_maxval': 255,
'target_minval': 0,
'target_maxval': 1
}))
preprocessing_options.append((preprocessor.random_black_patches, {
'size_to_image_ratio': 0.5
}))
images = self.createTestImages()
tensor_dict = {fields.InputDataFields.image: images}
blacked_tensor_dict = preprocessor.preprocess(tensor_dict,
preprocessing_options)
blacked_images = blacked_tensor_dict[fields.InputDataFields.image]
images_shape = tf.shape(images)
blacked_images_shape = tf.shape(blacked_images)
return [images_shape, blacked_images_shape]
(images_shape_, blacked_images_shape_) = self.execute_cpu(graph_fn, [])
self.assertAllEqual(images_shape_, blacked_images_shape_) 示例3: test_build_random_black_patches
# 需要導入模塊: from object_detection.core import preprocessor [as 別名]
# 或者: from object_detection.core.preprocessor import random_black_patches [as 別名]
def test_build_random_black_patches(self):
preprocessor_text_proto = """
random_black_patches {
max_black_patches: 20
probability: 0.95
size_to_image_ratio: 0.12
}
"""
preprocessor_proto = preprocessor_pb2.PreprocessingStep()
text_format.Merge(preprocessor_text_proto, preprocessor_proto)
function, args = preprocessor_builder.build(preprocessor_proto)
self.assertEqual(function, preprocessor.random_black_patches)
self.assert_dictionary_close(args, {'max_black_patches': 20,
'probability': 0.95,
'size_to_image_ratio': 0.12})